SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1876–1900 of 3304 papers

TitleStatusHype
Challenging Euclidean Topological AutoencodersCode0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Invertible Manifold Learning for Dimension ReductionCode0
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins—0
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler DivergenceCode0
Factorized Discriminant Analysis for Genetic Signatures of Neuronal PhenotypesCode0
Bayesian Feature Selection in Joint Quantile Time Series Analysis—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
Deep matrix factorizations—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Evaluation of company investment value based on machine learning—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Deep Monocular Visual Odometry for Ground Vehicle—0
Compact Learning for Multi-Label Classification—0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified